Intelligent Systems Theory

Major: Computer Systems and Networks
Code of subject: 7.123.01.O.004
Credits: 4.00
Department: Electronic Computing Machines
Lecturer: PhD, associate professor Botchkaryov Oleksy Yuriyovich
Semester: 1 семестр
Mode of study: денна
Learning outcomes: to know the general principles, purpose and architecture of the autonomous intelligent systems; to understand conceptual foundations of the functioning of the main modules implementing basic functionality of the autonomous intelligent systems; to know the principles of machine learning, in particular the principles and methods of reinforcement learning; to understand the principles of collective behavior of intelligent agents and foundations of multi-agent systems; to be able to design, configure and adjust the autonomous intelligent systems.
Required prior and related subjects: prerequisites: Computer logics, Computer systems, corequisites: Computer systems of artificial intelligence
Summary of the subject: The concept of an intelligent system. Mathematical modeling of the simple forms of purposeful behavior. Learning automata. Machine learning. Reinforcement learning. Markov decision process (MDP). Architecture of an intelligent agent. Multi-agent systems (MAS). Algorithms and software for MAS. Coordination of collective behavior of the intelligent agents. The concept of self-organization. Self-organization in autonomous decentralized systems.
Assessment methods and criteria: Written reports on laboratory work, the verbal questioning (40%) Final assessment (60 %, control method, exam): written-verbal form (60%)
Recommended books: 1. Stuart Russell, Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd edition, Pearson, 2009. - 1152 p. 2. David L. Poole, Alan K. Mackworth, Artificial Intelligence: Foundations of Computational Agents, Cambridge University Press, 2010. - 682 p. 3. Multiagent Systems, by Gerhard Weiss (Editor), 2nd edition, The MIT Press, 2013. - 920 p.